Evolutionary neural networks applied to keystroke dynamics: Genetic and immune based

The evolution in the use of digital identities has brought several advancements. However, this evolution has also contributed to the rise of the identity theft. An alternative to curb identity theft is by the identification of anomalous user behavior on the computer, what is known as behavioral intrusion detection. Among the features to be extracted from the user behavior, this paper focuses on keystroke dynamics, which analysis the user typing rhythm. This work uses a neural network to recognize users by keystroke dynamics and draws a comparison among several training algorithms: single backpropagation, three approaches based on genetic algorithms and three approaches based on immune algorithms.